{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 序列预测\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "from torch import nn, optim\n",
    "from torch.autograd import Variable\n",
    "from torch.nn import init"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 已知\n",
    "1949年到1960年每一个月的飞机客流量"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAAEICAYAAABRSj9aAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f184009f4a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 获取输入数据，并可视化\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "% matplotlib inline\n",
    "\n",
    "data = pd.read_csv('data.csv', usecols=[1])\n",
    "plt.title('International airline passengers')\n",
    "plt.plot(data)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "## 目标\n",
    "给定前三个月的客流量预测当前月的飞机客流量。\n",
    "\n",
    "以1949年到1958年的数据作为训练集， 1959和1960年的数据用作测试集。\n",
    "## 方法\n",
    "1. 模型使用某三个月的数据作为输入，获得第四个月的客流量\n",
    "2. 以此类推进行测试\n",
    "\n",
    "## 比较\n",
    "创建三个模型进行结果比较 : `FC`, `RNN`, `LSTM`, `GRU1`, `GRU2`\n",
    "\n",
    "`GRU2`比`GRU1`多一般的状态数\n",
    "\n",
    "分别从预测准确性，训练时间两方面分析。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "**以下部分为数据预处理的代码，会在其他几个模型中动态生成\\*.py文件并引用**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 定义numpy转化为Tensor的函数\n",
    "toTs = lambda x : torch.from_numpy(x)\n",
    "\n",
    "# 定义检测cuda的函数\n",
    "cudAvl = lambda x : x.cuda() if torch.cuda.is_available() else x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dataSet shape :\t (144, 1)\n",
      "train data shape : (120, 1)\n",
      "real data shape : (144, 1)\n"
     ]
    }
   ],
   "source": [
    "# 数据预处理\n",
    "data = data.dropna()\n",
    "dataSet = data.values\n",
    "dataSet = dataSet.astype('float32')\n",
    "print('dataSet shape :\\t', dataSet.shape)\n",
    "\n",
    "# 数据归一化\n",
    "def MinMaxScaler(X) :\n",
    "    mx, mi = np.max(X), np.min(X)\n",
    "    X_std = (X - mi) / (mx - mi)\n",
    "    return X_std\n",
    "\n",
    "dataSet = MinMaxScaler(dataSet)\n",
    "\n",
    "# 将数据分为训练集和测试集\n",
    "train = dataSet[:12*10]\n",
    "real = dataSet\n",
    "print('train data shape :', train.shape)\n",
    "print('real data shape :', real.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "input_size = 3"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
